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Reduced Honeybee Pollen Foraging under Neonicotinoid Exposure: Exploring Reproducible Individual and Colony Level

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Summary
This summary is machine-generated.

Sublethal pesticide exposure impairs honeybee (Apis mellifera) foraging, reducing pollen collection. Combining field studies with AI and simulations revealed individual bee behavior changes that impact colony sustainability, informing pesticide risk assessments.

Keywords:
Apis melliferaOomen studyautomated monitoringcomputational modelingfeeding study designneurotoxic effectpesticide exposurepollen foraging

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Area of Science:

  • Ecology
  • Entomology
  • Environmental Science

Background:

  • Honeybees (Apis mellifera) are crucial pollinators, with foraging behavior vital for colony survival.
  • Sublethal doses of pesticides, particularly neonicotinoids, can disrupt essential honeybee foraging activities, especially pollen collection.

Purpose of the Study:

  • To investigate the effects of sublethal imidacloprid exposure on honeybee foraging at individual and colony levels.
  • To integrate field experiments, AI monitoring, and mechanistic simulations (BEEHAVE model) to understand pesticide impacts.

Main Methods:

  • Conducted field experiments with sublethal doses of imidacloprid on honeybees.
  • Utilized artificial intelligence (AI)-based monitoring technology for tracking foraging behavior.
  • Employed the BEEHAVE mechanistic model to simulate and corroborate experimental findings.

Main Results:

  • Confirmed that imidacloprid selectively reduces pollen foraging at the colony level, with minor effects on nectar foraging.
  • Observed that exposed individual honeybees had longer pollen foraging trips, lower foraging frequency, and increased trip drifting.
  • Individual behavioral changes aligned with BEEHAVE model predictions, validating simulation approaches.

Conclusions:

  • Sublethal pesticide effects on individual honeybees, such as impaired cognition and metabolism, scale up to impact colony foraging dynamics.
  • Combining experimental data with mechanistic modeling provides a powerful approach to understanding pesticide impacts on pollinators.
  • The study offers a robust design for evaluating pesticide effects in realistic landscapes, enhancing ecological risk assessments.